Abstract
Building personalized task-oriented dialogue system is an important but challenging task. Significant success has been achieved by selecting the responses from the pre-defined template. However, preparing massive response template is time-consuming and human-labor intensive. In this paper, we propose an end-to-end framework based on the memory networks for responses generation in the personalized task-oriented dialog system. The static attention mechanism is used to encode the user-conversation relationship to form a global vector representation, and the dynamic attention mechanism is used to obtain import local information during the decoding phase. In addition, we propose a gating mechanism to incorporate user information into the network to enhance the personalized ability of the response. Experiments on the benchmark dataset show that our model achieves better performance than the strong baseline methods in personalized task-oriented dialogue generation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wen, T.H., Vandyke, D., Mrksic, N., et al.: A network-based end-to-end trainable task-oriented dialogue system. arXiv preprint arXiv:1604.04562 (2016)
Chen, H., Liu, X., Yin, D., et al.: A survey on dialogue systems: recent advances and new frontiers. ACM SIGKDD Explor. Newsl. 19(2), 25–35 (2017)
Bordes, A., Boureau, Y.L., Weston, J.: Learning end-to-end goal-oriented dialog. arXiv preprint arXiv:1605.07683 (2016)
Lei, W., Jin, X., Kan, M.Y., Ren, Z., He, X., Yin, D.: Sequicity: simplifying task-oriented dialogue systems with single sequence-to-sequence architectures. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 1437–1447 (2018)
Madotto, A., Wu, C.S., Fung, P.: Mem2Seq: effectively incorporating knowledge bases into end-to-end task-oriented dialog systems. arXiv preprint arXiv:1804.08217 (2018)
Wu, C.S., Madotto, A., Winata, G., Fung, P.: End-to-end recurrent entity network for entity-value independent goal-oriented dialog learning. In: Dialog System Technology Challenges Workshop, DSTC6 (2017)
Joshi, C.K., Mi, F., Faltings, B.: Personalization in goal-oriented dialog. arXiv preprint arXiv:1706.07503 (2017)
Mo, K., Zhang, Y., Li, S., Li, J., Yang, Q.: Personalizing a dialogue system with transfer reinforcement learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Yang, M., et al.: Investigating deep reinforcement learning techniques in personalized dialogue generation. In: Proceedings of the 2018 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2018)
Herzig, J., Shmueli-Scheuer, M., Sandbank, T., Konopnicki, D.: Neural response generation for customer service based on personality traits. In: Proceedings of the 10th International Conference on Natural Language Generation (2017)
Luo, L., Huang, W., Zeng, Q., Nie, Z., Sun, X.: Learning personalized end-to-end goal-oriented dialog. arXiv preprint arXiv:1811.04604 (2018)
Wu, C.S., Socher, R., Xiong, C.: Global-to-local memory pointer networks for task-oriented dialogue. arXiv preprint arXiv:1901.04713 (2019)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Syntax, Semantics and Structure in Statistical Translation, p. 103 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Li, J., Galley, M., Brockett, C., Spithourakis, G.P., Gao, J., Dolan, B.: A persona-based neural conversation model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers (2016)
Luan, Y., Brockett, C., Dolan, B., Gao, J., Galley, M.: Multi-task learning for speaker-role adaptation in neural conversation models. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing, vol. 1: Long Papers, pp. 605–614 (2017)
Yang, M., Tu, W., Qu, Q., Zhao, Z., Chen, X., Zhu, J.: Personalized response generation by dual-learning based domain adaptation. Neural Netw. 103, 72–82 (2018)
Mo, K., Li, S., Zhang, Y., Li, J., Yang, Q.: Personalizing a dialogue system with transfer learning. arXiv preprint arXiv:1610.02891 (2016)
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: I have a dog, do you have pets too? In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 2204–2213 (2018)
Sukhbaatar, S., Weston, J., Fergus, R.: End-to-end memory networks. In: Advances in Neural Information Processing Systems (2015)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning, December 2014 (2014)
Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
Liu, F., Perez, J.: Gated end-to-end memory networks. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol. 1, Long Papers (2017)
Gulcehre, C., Ahn, S., Nallapati, R., Zhou, B., Bengio, Y.: Pointing the unknown words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, vol. 1 (2016)
Eric, M., Manning, C.D.: A copy-augmented sequence-to-sequence architecture gives good performance on task-oriented dialogue. In: EACL, vol. 2017, p. 468 (2017)
Acknowledgement
This research was supported in part by NSFC under Grant Nos. No. U1836107, 61572158 and 61602132.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, B., Xu, X., Li, X., Ye, Y., Chen, X., Sun, L. (2019). Learning Personalized End-to-End Task-Oriented Dialogue Generation. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-32233-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32232-8
Online ISBN: 978-3-030-32233-5
eBook Packages: Computer ScienceComputer Science (R0)